So our study in single-cell analysis in words from patients undergoing ibrutinib therapy checked on the tumor and immune microenvironment of patients undergoing ibrutinib therapy. So focusing on the tumor cells we do see distinct gene expression And particularly, we do see that GEX-1 and 2 signatures are associated with a poor impact on progression-free survival. And we do also see that the gene expression signature 3 is associated with a much better outcome for these patients...
So our study in single-cell analysis in words from patients undergoing ibrutinib therapy checked on the tumor and immune microenvironment of patients undergoing ibrutinib therapy. So focusing on the tumor cells we do see distinct gene expression And particularly, we do see that GEX-1 and 2 signatures are associated with a poor impact on progression-free survival. And we do also see that the gene expression signature 3 is associated with a much better outcome for these patients. So although these signatures were predetermined by the CXCR4 mutation status, the CXCR4 mutation alone did not have any impact on the progression-free survival. So we do see that the genes now that are linked to this mutation pattern are working so much better in predicting ibrutinib response. Now, in the immune microenvironment, we do see a few changes at the time of the diagnosis. However, the most distinct ones are after therapy. So these are the exhaustion signatures after therapy, which are much higher in the non-responder group, along with Tregs, and the inflammatory pathways are activated after therapy in the non-responder group. So what I think is that the response is not so much dependent on the baseline of the immune cells, but it’s more on the capacity of these cells to be reprogrammed after protein therapy. So I think, like overall, these gene features as well as the immune phenotype can be incorporated in the routine practice in the next few years and this needs to be supported by other groups so we can go beyond the mid-88 and CXR4 mutation status as well as the IgM response and be more precise on how these patients will respond to ibrutinib therapies or even other BTKI inhibitors.
Yes, I think we are very close in using gene features. So from our single cell analysis, we show that there are a few genes, just about 20 genes, that can be predictive for ibrutinib response. So we can just do a screen of these genes, the expression of these genes, and we can also do flow cytometry analysis for the immune phenotype of these patients. So we can use all of this information to be much more precise on the response to these patients.